CNN Cloud Detection Algorithm Based on Channel and Spatial Attention and Probabilistic Upsampling for Remote Sensing Image

In the field of remote sensing image, how to transmit image information more efficiently with limited bandwidth has always been a research hotspot. Compared with other ground objects, cloud pixels in remote sensing image are invalid information, so it is a meaningful research work to remove cloud before transmitting image and reduce the waste of useless information. In remote sensing image, due to the existence of thin clouds and the complexity of the underlying surface, most of the cloud detection algorithms struggle to achieve effective separation of clouds and ground objects. A deep learning (DL) cloud detection algorithm based on attention mechanism and probability upsampling has been proposed in this article. In order to enhance the information of the key areas, in the channel attention module, crucial information is highlighted in the channel dimension of the encoder, and the useless information is weakened. The spatial attention module is in the spatial dimension. The information fusion between each point in the image is strengthened. To reduce the information loss caused by the down-sampling module, a probabilistic upsampling block (PUB) is proposed to restore the image. Eventually, experiments are performed on Gaofen-1WFV data, and the results indicate that the algorithm proposed in this article has better detection results than other cloud detection algorithms in different scenarios.
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